Down the rabbit hole: Robust proximity search and density estimation in sublinear space

For a set of n points in ℝ^d, and parameters k and ε, we present a data structure that answers (1 + ε)-approximate k nearest neighbor queries in logarithmic time. Surprisingly, the space used by the data-structure is Õ(n/k), that is, the space used is sub linear in the input size if k is sufficiently large. Our approach provides a novel way to summarize geometric data, such that meaningful proximity queries on the data can be carried out using this sketch. Using this we provide a sub linear space data-structure that can estimate the density of a point set under various measures, including: (i) sum of distances of k closest points to the query point, and (ii) sum of squared distances of k closest points to the query point. Our approach generalizes to other distance based estimation of densities of similar flavor.